The aim of this project is to adapt existing statistical methods, and to develop new ones where needed, in order to meet the specific needs of human pharmacogenetics, with special application to the study of alcoholism and related phenotypes. We will emphasize methods for: (1) the genetic modeling of multivariate polytomous data, which are frequently encountered and for which traditional multivariate QTL mapping methods may not be applicable; (2) gene mapping methods with dose-response models for the analysis of data collected in experimental settings (e.g., an drug challenge study); and (3) optimal study design methodology which integrates prior knowledge of known risk factors (e.g., candidate genes, environmental risk factors) with mild assumptions regarding the mode of transmission of the trait in the planning of gene mapping studies (experimental or epidemiological). Secondary data analyses will be used for reality-testing of methodological research, and will utilize an existing database consisting of questionnaire and interview data on patterns of smoking, alcohol and drug use, and alcoholism, obtained on more than 14,000 family members in Australia. The methods to be developed as part of this project will be implemented in an integrated library of C++ functions. These functions will allow: (1) multipoint linkage analysis of multivariate continuous and polytomous data, with covariates (including experimental factors); (2) power computation and optimal study design, first with fast asymptotic methods (for a first impression) and, second, with simulation experiments (for confirmation); (3) certain database management capabilities, obviating much of the need to use multiple programs in the course of an analysis, and thus reducing the risks of error.